Checking the homoscedasticity assumption of an lme model

I have the following lme model, which I am running using the following code:

IDRTlme <- lme(Score ~ Group*Condition, random = ~1|ID, data=IDRT)

I would like to carry out the levene's test as follows:

leveneTest(residuals(IDRTlme) ~ IDRT$Group)

Is this the correct way of carrying out a Levene's test on an lme output?

If not, then I would very much appreciate any assistance.

I would be so grateful for any help!

Here is the data for IDRT:

structure(list(ID = c("1993", "1993", "1993", "1993", "1993", 
"1993", "1997", "1997", "1997", "1997", "1997", "1997", "19998", 
"19998", "19998", "19998", "19998", "19998", "3122", "3122", 
"3122", "3122", "3122", "3122", "3152", "3152", "3152", "3152", 
"3152", "3152", "3182", "3182", "3182", "3182", "3182", "3182", 
"330", "330", "330", "330", "330", "330", "354", "354", "354", 
"354", "354", "354", "363", "363", "363", "363", "363", "363", 
"369", "369", "369", "369", "369", "369", "370", "370", "370", 
"370", "370", "370", "375", "375", "375", "375", "375", "375", 
"377", "377", "377", "377", "377", "377", "378", "378", "378", 
"378", "378", "378", "379", "379", "379", "379", "379", "379", 
"380", "380", "380", "380", "380", "380", "381", "381", "381", 
"381", "381", "381", "3862", "3862", "3862", "3862", "3862", 
"3862", "3872", "3872", "3872", "3872", "3872", "3872", "388", 
"388", "388", "388", "388", "388", "390", "390", "390", "390", 
"390", "390", "392", "392", "392", "392", "392", "392", "393", 
"393", "393", "393", "393", "393", "394", "394", "394", "394", 
"394", "394", "395", "395", "395", "395", "395", "395", "396", 
"396", "396", "396", "396", "396", "399", "399", "399", "399", 
"399", "399", "5512", "5512", "5512", "5512", "5512", "5512", 
"382", "382", "382", "382", "382", "382", "1001", "1001", "1001", 
"1001", "1001", "1001", "1002", "1002", "1002", "1002", "1002", 
"1002", "1003", "1003", "1003", "1003", "1003", "1003", "1004", 
"1004", "1004", "1004", "1004", "1004", "1005", "1005", "1005", 
"1005", "1005", "1005", "1006", "1006", "1006", "1006", "1006", 
"1006", "1007", "1007", "1007", "1007", "1007", "1007", "1008", 
"1008", "1008", "1008", "1008", "1008", "1009", "1009", "1009", 
"1009", "1009", "1009", "1012", "1012", "1012", "1012", "1012", 
"1012", "1013", "1013", "1013", "1013", "1013", "1013", "1014", 
"1014", "1014", "1014", "1014", "1014", "1015", "1015", "1015", 
"1015", "1015", "1015", "1016", "1016", "1016", "1016", "1016", 
"1016", "1017", "1017", "1017", "1017", "1017", "1017", "1020", 
"1020", "1020", "1020", "1020", "1020", "1021", "1021", "1021", 
"1021", "1021", "1021", "1024", "1024", "1024", "1024", "1024", 
"1024", "1025", "1025", "1025", "1025", "1025", "1025", "1026", 
"1026", "1026", "1026", "1026", "1026", "1027", "1027", "1027", 
"1027", "1027", "1027", "1088", "1088", "1088", "1088", "1088", 
"1088", "1192", "1192", "1192", "1192", "1192", "1192", "1422", 
"1422", "1422", "1422", "1422", "1422", "1492", "1492", "1492", 
"1492", "1492", "1492", "1592", "1592", "1592", "1592", "1592", 
"1592", "1602", "1602", "1602", "1602", "1602", "1602", "1642", 
"1642", "1642", "1642", "1642", "1642", "171", "171", "171", 
"171", "171", "171", "1722", "1722", "1722", "1722", "1722", 
"1722", "1732", "1732", "1732", "1732", "1732", "1732", "174", 
"174", "174", "174", "174", "174", "175", "175", "175", "175", 
"175", "175", "1752", "1752", "1752", "1752", "1752", "1752", 
"1762", "1762", "1762", "1762", "1762", "1762", "1782", "1782", 
"1782", "1782", "1782", "1782", "1802", "1802", "1802", "1802", 
"1802", "1802", "182", "182", "182", "182", "182", "182", "184", 
"184", "184", "184", "184", "184", "1852", "1852", "1852", "1852", 
"1852", "1852", "186", "186", "186", "186", "186", "186", "187", 
"187", "187", "187", "187", "187", "188", "188", "188", "188", 
"188", "188", "1892", "1892", "1892", "1892", "1892", "1892", 
"190", "190", "190", "190", "190", "190", "192", "192", "192", 
"192", "192", "192", "1924", "1924", "1924", "1924", "1924", 
"1924", "193", "193", "193", "193", "193", "193", "195", "195", 
"195", "195", "195", "195", "196", "196", "196", "196", "196", 
"196", "197", "197", "197", "197", "197", "197", "1982", "1982", 
"1982", "1982", "1982", "1982", "1992", "1992", "1992", "1992", 
"1992", "1992", "19922", "19922", "19922", "19922", "19922", 
"19922", "1999", "1999", "1999", "1999", "1999", "1999", "19992", 
"19992", "19992", "19992", "19992", "19992", "199924", "199924", 
"199924", "199924", "199924", "199924", "199945", "199945", "199945", 
"199945", "199945", "199945", "199949", "199949", "199949", "199949", 
"199949", "199949", "199951", "199951", "199951", "199951", "199951", 
"199951", "199952", "199952", "199952", "199952", "199952", "199952", 
"199j2", "199j2", "199j2", "199j2", "199j2", "199j2", "490", 
"490", "490", "490", "490", "490", "181", "181", "181", "181", 
"181", "181", "3812", "3812", "3812", "3812", "3812", "3812", 
"199950", "199950", "199950", "199950", "199950", "199950", "191", 
"191", "191", "191", "191", "191"), Condition = structure(c(1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("neutral", 
"neutral_social", "no_money", "positive_social", "selfharm", 
"win_money"), class = "factor"), Score = c(0.221611076, 0.206888887611111, 
0.2319999696, 0.228521740956522, 0.206187486625, 0.220866648533333, 
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0.213087154478261, 0.186750114, 0.20812016488, 0.231142997714286, 
0.21812013628, 0.252904755761905, 0.241294103411765, 0.24183999076, 
0.240500022916667, 0.254666725666667, 0.247681823681818, 0.225026962054054, 
0.210073992074074, 0.224115325961538, 0.222111026527778, 0.217529331970588, 
0.218382267529412, 0.241200140457143, 0.237963155333333, 0.245565362434783, 
0.245676636735294, 0.23380016328, 0.234291831625, 0.259823546764706, 
0.2422999461, 0.251066668933333, 0.22864999775, 0.262578926526316, 
0.233666698111111, 0.26059517397619, 0.24753120528125, 0.252969611787879, 
0.266944355416667, 0.251853599804878, 0.257299917925, 0.240636370454545, 
0.250892877571429, 0.269588218, 0.240586206793103, 0.232749988583333, 
0.250000008888889, 0.258468075, 0.251829781361702, 0.257641009794872, 
0.249021266404255, 0.254190473309524, 0.241840915295455, 0.282767379023256, 
0.275352863647059, 0.274970496323529, 0.288102498358974, 0.274428497, 
0.259230674769231, 0.26825004825, 0.217374995375, 0.247444417777778, 
0.230684167421053, 0.161000013333333, 0.207999997555556, 0.254157882, 
0.2496999265, 0.2209999565, 0.25166670475, 0.26030000445, 0.242944452611111, 
0.306541830291667, 0.277034636172414, 0.27975015334375, 0.281909249090909, 
0.31016016016, 0.279727430060606, 0.231699951633333, 0.226371377, 
0.235916574833333, 0.228586147758621, 0.230285636214286, 0.240481412037037, 
0.242555611666667, 0.25845010875, 0.231857257142857, 0.249205210333333, 
0.247047696761905, 0.232548459967742, 0.373833229125, 0.331785661785714, 
0.3506665866, 0.326083252791667, 0.357047535047619, 0.372882268, 
0.282382425088235, 0.264577012615385, 0.278575868333333, 0.2825625985, 
0.27254177125, 0.275424350333333, 0.256368611868421, 0.261394081606061, 
0.259485006242424, 0.268589887692308, 0.241216395324324, 0.25932275083871, 
0.240428498821429, 0.238733259866667, 0.23574991225, 0.244956472608696, 
0.248870880516129, 0.2653124034375, 0.248142810047619, 0.264461517307692, 
0.261307716153846, 0.238578984631579, 0.277941198882353, 0.229461559923077, 
0.27259369190625, 0.273410173461538, 0.26809083330303, 0.284166589375, 
0.282264646352941, 0.265302954272727, 0.258647063117647, 0.2565000355, 
0.254529392058824, 0.232392856107143, 0.22980002155, 0.245047626047619, 
0.279326019043478, 0.263157850868421, 0.286130366043478, 0.275814745185185, 
0.281524956225, 0.265363541636364, 0.229149919825, 0.2269999161875, 
0.214912963956522, 0.22574996955, 0.213696906212121, 0.221666583407407, 
0.295230801230769, 0.294124990708333, 0.2932499945, 0.270708332333333, 
0.271035696714286, 0.279705889, 0.276954639909091, 0.285567663756757, 
0.278821510928571, 0.288344901206897, 0.21375000475, 0.270428637085714
), Group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
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1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L), .Label = c("HC", "SH"), class = "factor")), row.names = c(NA, 
-576L), class = c("tbl_df", "tbl", "data.frame"))

Hi I have covered all these in my video on my channel. Kindly review Linear Regression using R Programming part 1 of 2 - YouTube
Hope I understood your requirements
Good Day